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Solmaz Rastegar M M*

School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand

Publications

  • Research Article   
    Systolic Blood Pressure Estimation from Electrocardiogram and Photo Plethysmogram Signals Using Convolutional Neural Networks
    Author(s): Solmaz Rastegar M M*, Hamid Gholamhosseini, Andrew Lowe and Maria Lindén

    Background: Digital continuous blood pressure (BP) monitoring is increasingly being used in clinical and remote settings. Although it could significantly help clinicians in vital signs monitoring, the analyzing of such amount of BP data is challenging. Objective: This study is aimed to investigate the feasibility of applying deep convolutional neural network (CNN) to the estimation of the systolic blood pressure (SBP) using electrocardiogram (ECG) and photo plethysmography (PPG) signals. Method: A total of 62500 ECG and PPG signals, sampled at 125 Hz, with 250 corresponding SBP, sampled at 1 Hz, were selected from Medical Information Mart for Intensive Care (MIMIC-III) Waveform Database. The collected signals from 22 subjects were divided into training (80%) and testing (20%) datasets. A CNN-based model was designed with five convolutional lay.. Read More»